Author: Dalibor Stevanovic
Publisher:
ISBN: 9780494813737
Category :
Languages : en
Pages :

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Book Description

Author: Siem Jan Koopman
Publisher: Emerald Group Publishing
ISBN: 1785603523
Category : Business & Economics
Languages : en
Pages : 685

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Book Description
This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.

Author: Jean-Marie Dufour
Publisher:
ISBN:
Category :
Languages : en
Pages : 47

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Book Description
We study the relationship between VARMA and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors cannot both follow finite-order VAR models. Instead, a VAR factor dynamics induces a VARMA process, while a VAR process entails VARMA factors. We propose to combine factor and VARMA modeling by using factor-augmented VARMA (FAVARMA) models. This approach is applied to forecasting key macroeconomic aggregates using large U.S. and Canadian monthly panels. The results show that FAVARMA models yield substantial improvements over standard factor models, including precise representations of the effect and transmission of monetary policy.

Author: Jean-Marie Dufour
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description

Author: Jushan Bai
Publisher: Now Publishers Inc
ISBN: 1601981449
Category : Business & Economics
Languages : en
Pages : 90

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Book Description
Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.

Author:
Publisher:
ISBN:
Category : Macroeconomics
Languages : en
Pages : 109

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Book Description
This thesis utilizes factor models to test the predictions of macroeconomic theory and introduces a new model for estimating structural relations in the economy. Factor models have proven useful in overcoming limited information bias. Limited information bias occurs because the information set of the actual decision makers in the economy is larger than the information set captured by conventional empirical models (i.e. small VARs). With the help of factors we can model a large dataset by using a small model of factors that still capture the majority of aggregate dynamics in the economy. In the rst chapter, joint work with Massimiliano Marcellino, we introduce a new empir- ical model: mixed frequency structural factor augmented VAR model. We show that in a mixed data frequency setting the model reduces aggregation bias and provides more precise estimates of factors and impulse responses, than competing models. We support this claim by means of a detailed Monte Carlo examination that also tests the new estimation procedure that we design. Finally we provide three empirical applications (monetary policy, oil and government expenditure shock) to show the usefulness of the model. In the second chapter I utilize a dynamic factor model to test the predictions of the rational inattention theory as put forward by Mackoviak et al. (2009). I rst estimate a time varying parameter dynamic factor model on US post-war data on macroeconomic variables and sector prices. I identify impulse responses of three macroeconomic shocks and sector speci c shocks to prices. I then regress price impulse responses, void of the in uences of changing variances, on the variances of the shocks, to test the predictions of the rational inattention model over time.

Author: Laurent Callot
Publisher:
ISBN:
Category :
Languages : en
Pages : 32

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Book Description
This paper shows that the parsimoniously time-varying methodology of Callot and Kristensen (2015) can be applied to factor models. We apply this method to study macroeconomic instability in the US from 1959:1 to 2006:4 with a particular focus on the Great Moderation. Models with parsimoniously time-varying parameters are models with an unknown number of break points at unknown locations. The parameters are assumed to follow a random walk with a positive probability that an increment is exactly equal to zero so that the parameters do not vary at every point in time. The vector of increments, which is high dimensional by construction and sparse by assumption, is estimated using the Lasso.We apply this method to the estimation of static factor models and factor augmented autoregressions using a set of 190 quarterly observations of 144 US macroeconomic series from Stock and Watson (2009). We find that the parameters of both models exhibit a higher degree of instability in the period from 1970:1 to 1984:4 relative to the following 15 years. In our setting the Great Moderation appears as the gradual ending of a period of high structural instability that took place in the 1970s and early 1980s.

Author: Karim Barhoumi
Publisher:
ISBN:
Category :
Languages : en
Pages : 54

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Book Description
La version française de cet article peut être consultée à: "http://ssrn.com/abstract=2243625" http://ssrn.com/abstract=2243625For few years, the increasing size of available economic and financial databases has led econometricians to develop and adapt new methods in order to efficiently summarize information contained in those large datasets. Among those methods, dynamic factor models have known a rapid development and a large success among macroeconomists. In this paper, we carry out a review of the recent literature on dynamic factor models. First we present the models used, then the parameter estimation methods and finally the statistical tests available to choose the number of factors. In the last section, we focus on recent empirical applications, especially dealing with the building of economic outlook indicators, macroeconomic forecasting and macroeconomic and monetary policy analyses.

Author: Xu Han
Publisher:
ISBN:
Category :
Languages : en
Pages : 67

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Book Description
We develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, conventional tests cannot be used. Based on the fact that the presence of a structural change in factor loadings yields a structural change in second moments of factors obtained from the full sample principal component estimation, we reduce the infinite-dimensional problem into a finite-dimensional one and our statistic compares the pre- and post-break subsample second moments of estimated factors. Our test is consistent under the alternative hypothesis in which a fraction of or all factor loadings have structural changes. The Monte Carlo results show that our test has good finite-sample size and power.

Author: Peng Huang (Economist)
Publisher:
ISBN:
Category : Foreign exchange rates
Languages : en
Pages : 0

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Book Description
This dissertation focuses on applying time-varying-parameter models to the field of financial and monetary economics. The first two essays analyze the cross-sectional returns on the U.S. stock market by emphasizing the dynamics of risk loadings. The third essay studies the impact of a tight monetary policy on weak currencies during financial crises by examining the time-varying relationship between interest rates and exchange rates. Motivated by the pricing errors found in small size and low book-to-market ratio portfolios in the Fama-French three-factor model, the first essay proposes a time-varying four-factor model. As small size and low book-to-market ratio firms are more sensitive to the risk related to innovations in the discount rate, the model incorporates a new risk factor to capture the information about the discount-rate risk for which the Fama-French three factors cannot fully account. In addition, the investors' learning process mimicked by the Kalman filter procedure is used to model the evolution of risk loadings. The results indicate that the model outperforms the Fama-French three-factor model in explaining the cross-sectional returns by substantially reducing pricing errors. The second essay analyzes the risk-return relationship in a capital asset pricing model (CAPM) with a time-varying beta estimated by adaptive least squares (ALS) based on Kalman foundations. The results show the presence of a significant and positive risk-return relationship in the up market and the presence of a significant and negative risk-return relationship in the down market. In comparison with the model that assumes a constant beta, the CAMP with a time-varying beta reduces unexplained returns and improves the accuracy of the estimated risk-return relationship. The third essay investigates the use of interest rates as a monetary instrument to stabilize exchange rates in the Asian financial crisis. Since previous studies suggest that the interest-exchange rate relationship may vary within, or across, regimes, a time-varying-parameter model with generalized autoregressive conditional heteroskedastic (GARCH) disturbances is used to estimate the impact of raising interest rates on exchange rates. The empirical evidence shows that an increase in interest rates leads to currency depreciation during certain periods of financial crises.